This article was meant to be entirely reproducible, with the data and code published alongside the article. It is however not embedded within a container (e.g. Docker). Will it past the reproducibility test tomorrow? next year? I'm curious.
We think this is an interesting paper for anyone who wants to learn to build an API with the R package plumber. This is a novel method in health economics, but we believe will help improve the transparency of modelling methods in our field.
This paper proposes a probabilistic planner that can solve goal-conditional tasks such as complex continuous control problems. The approach reaches state-of-the-art performance when compared to current deep reinforcement learning algorithms. However, the method relies on an ensemble of deep generative models and is computationally intensive. It would be interesting to reproduce the results presented in this paper on their robotic manipulation and navigation problems as these are very challenging problems that current reinforcement learning methods cannot easily solve (and when they do, they require a significantly larger number of experiences). Can the results be reproduced out-of-the-box with the provided code?
Even though the approach in the paper focuses on a specific measurement (clumped isotopes) and how to optimize which and how many standards we use, I hope that the problem is general enough that insight can translate to any kind of measurement that relies on machine calibration. I've committed to writing a literate program (plain text interspersed with code chunks) to explain what is going on and to make the simulations one step at a time. I really hope that this is understandable to future collaborators and scientists in my field, but I have not had any code review internally and I also didn't receive any feedback on it from the reviewers. I would love to see if what in my mind represents "reproducible code" is actually reproducible, and to learn what I can improve for future projects!
